REPOGEO REPORT · LITE

huggingface/evaluation-guidebook

Default branch main · commit e09b159d · scanned 5/21/2026, 4:33:04 PM

GitHub: 2,111 stars · 123 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface huggingface/evaluation-guidebook, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highabout#1

Update 'About' description to clarify archival status and guidebook type

Why:

CURRENT

Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!

COPY-PASTE FIX

An archived guidebook sharing practical insights and theoretical knowledge about LLM evaluation, gathered while managing the Open LLM Leaderboard and designing lighteval. For the latest version, see the linked homepage.

mediumhomepage#2
Add homepage URL pointing to the maintained version
Why:
COPY-PASTE FIX
```
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook
```

lowreadme#3

Add a section to README clarifying the license

Why:

COPY-PASTE FIX

## License
This guidebook is licensed under [insert specific license(s) here, e.g., CC-BY-NC-SA 4.0]. Please refer to the LICENSE file for full details.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface huggingface/evaluation-guidebook

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Weights & Biases

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Weights & Biases · recommended 2×
Hugging Face Evaluate · recommended 1×
LangChain Evaluation · recommended 1×
Arize AI · recommended 1×
Phoenix · recommended 1×

CATEGORY QUERY
How can I effectively measure the performance of large language models for specific tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Evaluate
2. LangChain Evaluation
3. Arize AI
4. Phoenix
5. Weights & Biases
6. DeepEval
7. Humanloop
8. OpenAI Evals
AI recommended 8 alternatives but never named huggingface/evaluation-guidebook. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are best practices for designing robust evaluation metrics for LLM applications?
you: not recommended
AI recommended (in order):
1. Scale AI
2. Appen
3. Surveymonkey
4. Google Forms
5. ROUGE
6. BLEU
7. METEOR
8. BERTScore
9. MoverScore
10. F1-score
11. Precision
12. Recall
13. Exact Match
14. Semantic Accuracy
15. Perplexity
16. Giskard
17. LangChain
18. Weights & Biases
19. MLflow
AI recommended 19 alternatives but never named huggingface/evaluation-guidebook. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of huggingface/evaluation-guidebook?
pass
AI did not name huggingface/evaluation-guidebook — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts huggingface/evaluation-guidebook in production, what risks or prerequisites should they evaluate first?
pass
AI named huggingface/evaluation-guidebook explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo huggingface/evaluation-guidebook solve, and who is the primary audience?
pass
AI named huggingface/evaluation-guidebook explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Pro

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huggingface/evaluation-guidebook — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite